SIMADL: Simulated Activities of Daily Living Dataset

Alshammari, Talal; Alshammari, Nasser; Sedky, Mohamed; Howard, Chris

doi:10.3390/data3020011

Cited by 36 publications

(39 citation statements)

References 19 publications

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“…This section presents the evaluation methodology for analyzing the efficacy of the ZASH system. We implemented and executed the ZASH system with Python 3.9.5 in Zorin OS 15.3 using SIMADL (Simulated Activities of Daily Living Dataset) [Alshammari et al 2018] and can be found at GitHub 1 . The technology was chosen for its portability, ease of file manipulation and ease of algorithms writing, providing quick development and evaluation.…”

Section: Methodsmentioning

confidence: 99%

Zero Trust Access Control with Context-Aware and Behavior-Based Continuous Authentication for Smart Homes

Silva¹,

Macedo²,

Santos³

2021

Anais Do XXI Simpósio Brasileiro De Segurança Da Informação E De Sistemas Computacionais (SBSeg 2021)

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Generally, approaches to build the security of Smart Home Systems (SHS) require big amount of data to implement Access Control and Intrusion Detection Systems, with storage in cloud, for instance, being a vulnerability to inhabitants privacy. Besides, most works rely on cloud computing or resources in the cloud to perform security tasks, what can be exploited by attackers. This work presents the ZASH (Zero-Aware Smart Home System), an Access Control for SHS. ZASH uses Continuous Authentication with Zero Trust, supported by real-time context and activity information, enabled by Edge Computing and Markov Chain, to prevent and mitigate impersonation attacks that aim to invade inhabitants privacy. An experimental evaluation demonstrated the system capability to dynamically adapt to new inhabitants behaviors withal blocking impersonation attacks.

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Section: Methodsmentioning

confidence: 99%

Zero Trust Access Control with Context-Aware and Behavior-Based Continuous Authentication for Smart Homes

Silva¹,

Macedo²,

Santos³

2021

Anais Do XXI Simpósio Brasileiro De Segurança Da Informação E De Sistemas Computacionais (SBSeg 2021)

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“…Zerkouk and Chikhaoui [ 27 ] proposed a method based on LSTM to estimate older people's abnormal behaviors. They used SIMADL [ 28 ] dataset in their experiments. A novel symptom-based “Unified Parkinson Disease Rating Scale” estimation system was presented by Hssayeni et al [ 14 ].…”

Section: Related Studiesmentioning

confidence: 99%

A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor

Tabrizi

Pashazadeh

Javani

2021

Computational Intelligence and Neuroscience

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Psychological and behavioral evidence suggests that home sports activity reduces negative moods and anxiety during lockdown days of COVID-19. Low-cost, nonintrusive, and privacy-preserving smart virtual-coach Table Tennis training assistance could help to stay active and healthy at home. In this paper, a study was performed to develop a Forehand stroke’ performance evaluation system as the second principal component of the virtual-coach Table Tennis shadow-play training system. This study was conducted to show the effectiveness of the proposed LSTM model, compared with 2DCNN and RBF-SVR time-series analysis and machine learning methods, in evaluating the Table Tennis Forehand shadow-play sensory data provided by the authors. The data was generated, comprising 16 players’ Forehand strokes racket’s movement and orientation measurements; besides, the strokes’ evaluation scores were assigned by the three coaches. The authors investigated the ML models’ behaviors changed by the hyperparameters values. The experimental results of the weighted average of RMSE revealed that the modified LSTM models achieved 33.79% and 4.24% estimation error lower than 2DCNN and RBF-SVR, respectively. However, the R ¯ 2 results show that all nonlinear regression models are fit enough on the observed data. The modified LSTM is the most powerful regression method among all the three Forehand types in the current study.

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“…An autoencoder [32] is a multi-layer neural network in which the desired output is the input itself. The aim of autoencoder is to learn more advanced feature representation in compressed representation to catch the most significant features of the training data [33].…”

Section: Autoencoder-cnn-lstmmentioning

confidence: 99%

Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models

Zerkouk

Chikhaoui

2020

Sensors

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The ability to identify and accurately predict abnormal behavior is important for health monitoring systems in smart environments. Specifically, for elderly persons wishing to maintain their independence and comfort in their living spaces, abnormal behaviors observed during activities of daily living are a good indicator that the person is more likely to have health and behavioral problems that need intervention and assistance. In this paper, we investigate a variety of deep learning models such as Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), CNN-LSTM and Autoencoder-CNN-LSTM for identifying and accurately predicting the abnormal behaviors of elderly people. The temporal information and spatial sequences collected over time are used to generate models, which can be fitted to the training data and the fitted model can be used to make a prediction. We present an experimental evaluation of these models performance in identifying and predicting elderly persons abnormal behaviors in smart homes, via extensive testing on two public data sets, taking into account different models architectures and tuning the hyperparameters for each model. The performance evaluation is focused on accuracy measure.However, dependent persons are often exposed to problems of various types that may cause them to perform activities of daily living incorrectly. Detecting abnormal behaviors is thus of great importance for dependent people, in order to ensure that activities are performed correctly without errors [5]. This will also ensure their safety and well-being.Detecting an anomaly in a person's activities of daily living (ADL) is usually done by detecting nonconformities with their usual ADL patterns. Various authors have used classical machine learning algorithms to achieve this [6,7]. Tele-health care requires systems with high accuracy, low computational time and minimal user intervention because data are becoming larger and more complex [8]. Deep learning architectures provide a way to automatically extract useful and meaningful spatial and temporal features from raw data without the need for data labeling, which is time consuming, complex and error prone [9,10]. This makes deep learning models easily generalizable to different contexts. LSTM is a powerful deep learning model for sequence prediction and anomaly detection in sequential data [11]. The LSTM model is able to extract temporal features with long-term relationships. CNN has powerful spatial feature extraction capabilities and the ability to detect abnormality, which is considered as a classification problem. The advantage in the use of CNN with LSTM is to combine their capabilities in terms of spatial and temporal features extraction. Autoencoder learns a compressed input representation, which is recommended for the high dimensional data collected from smart home. Therefore, the motivation to select our deep learning models is due to their high performance accuracy in terms of abnormal behavior detection in different research areas [12,13].This propert...

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SIMADL: Simulated Activities of Daily Living Dataset

Cited by 36 publications

References 19 publications

Zero Trust Access Control with Context-Aware and Behavior-Based Continuous Authentication for Smart Homes

Zero Trust Access Control with Context-Aware and Behavior-Based Continuous Authentication for Smart Homes

A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor

Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models

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